Associations of marker panel scores with feed intake and efficiency traits in beef cattle using preselected single nucleotide polymorphisms1
Bibliographic record
Abstract
Because of the moderate heritability and the expense associated with collecting feed intake data, effective selection for residual feed intake would be enhanced if marker-assisted evaluation were used for accurate estimation of genetic merit. In this study, a suite of genetic markers predictive of residual feed intake, DMI, and ADG were preselected using single-marker regression analysis, and the top 100 SNP were analyzed further to provide prediction equations for the traits. The data used consisted of 728 spring-born beef steers, offspring of a cross between a composite dam line and Angus, Charolais, or University of Alberta hybrid bulls. Feed intake data were collected over a 5-yr period, with 2 groups (fall-winter and winter-spring) tested every year. Training and validation data sets were obtained by splitting the data into 2 distinct sets, by randomly splitting the data into training and testing sets based on sire family (split 1) in 5 replicates or by retaining all animals with no known pedigree relationships as the validation set (split 2). A total of 37,959 SNP were analyzed by single-marker regression, of which only the top 100 that corresponded to a P-value <0.002 were retained. The 100 SNP were then analyzed using random regression BLUP, and only SNP that were jointly significant (P < 0.05) were included in the final marker panels. The marker effects from the selected panels were used to derive the molecular breeding values, which were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, with the weights being the allele substitution effects. The correlation between molecular breeding value and phenotype represented the accuracy of prediction. For all traits evaluated, accuracy across breeds was low, ranging between 0.007 and 0.414. Accuracy was least in data split 2, where the validation individuals had no pedigree relationship with animals in the training data. Given the low predictive ability observed, a large number of individuals may be needed for prediction when using such an admixed population. Further, these results suggest that breed composition of the target population in which the marker panels are likely to be used should be an important consideration when developing prediction equations across breeds, especially where an admixed population is used as the training data set.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".